Open Science and Artificial Intelligence for supporting the sustainability of the SRC Network: The espSRC case
J. Garrido, S. Sánchez-Expósito, A. Ruiz-Falcó, J. Ruedas, M. Á. Mendoza, V. Vázquez, M. Parra, J. Sánchez, I. Labadie, L. Darriba, J. Moldón, M. Rodriguez-Álvarez, J. Díaz, L. Verdes-Montenegro
TL;DR
The paper tackles the sustainability of the SKA SRCNet by detailing the espSRC's Green and Open Science initiatives. It presents concrete actions including energy-monitoring in the computing center, data-transfer minimization via edge-computation strategies, and a trusted timestamping service to ensure provenance and reproducibility. The work demonstrates an integrated approach combining sensor-based energy analytics, Rucio-driven data management tests, and Open Science infrastructure to reduce environmental impact while enabling scalable scientific collaboration. The findings highlight the feasibility and value of embedding energy-aware design and trust infrastructure in large-scale, data-intensive research networks such as SKA.
Abstract
The SKA Observatory (SKAO), a landmark project in radio astronomy, seeks to address fundamental questions in astronomy. To process its immense data output, approximately 700 PB/year, a global network of SKA Regional Centres (SR-CNet) will provide the infrastructure, tools, computational power needed for scientific analysis and scientific support. The Spanish SRC (espSRC) focuses on ensuring the sustainability of this network by reducing its environmental impact, integrating green practices into data platforms, and developing Open Science technologies to enable reproducible research. This paper discusses and summarizes part of the research and development activities that the team is conducting to reduce the SRC energy consumption at the espSRC and SRCNet. The paper also discusses fundamental research on trusted repositories to support Open Science practices.